IRO Journal on Sustainable Wireless Systems
IRO Journals
Volume 3 — Issue 2 — June 2021

Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection

https://doi.org/10.36548/jsws.2021.2.006
Subarna Shakya
Professor, Department of Electronics and Computer Engineering, Central Campus, Institute of Engineering, Tribhuvan University,
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How to Cite

Shakya, Subarna. 2021. “Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection”. IRO Journal on Sustainable Wireless Systems 3 (2): 118-27. https://doi.org/10.36548/jsws.2021.2.006.
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Keywords

— Machine learning
— Support vector machine
— Intrusion detection
— Wireless sensor networks
— Grey wolf optimization algorithm
Published: 14-06-2021

Abstract

The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.

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